Distributed relationship reasoning engine for generating analytics for hemodynamic instability

ABSTRACT

A reasoning engine is disclosed. Contemplated reasoning engines acquire data relating to one or more aspects of various environments. Inference engines within the reasoning engines review the acquire data, historical or current, to generate one or more hypotheses about how the aspects of the environments might be correlated, if at all. The reasoning engine can attempt to validate the hypotheses through controlling acquisition of the environment data.

This application claims the benefit of priority to U.S. provisionalapplication having Ser. No. 61/466,367 and U.S. provisional applicationhaving Ser. No. 61/466,398; both filed Mar. 22, 2011. These and allother extrinsic materials discussed herein are incorporated by referencein their entirety. Where a definition or use of a term in anincorporated reference is inconsistent or contrary to the definition ofthat term provided herein, the definition of that term provided hereinapplies and the definition of that term in the reference does not apply.

FIELD OF THE INVENTION

The field of the invention is artificial reasoning technologies.

BACKGROUND

The amount of digital data made available for analysis has grown alongwith the growth of computing technology and communicationinfrastructure. Unfortunately vast mountains of data remain unused orunviewed. Great discoveries remain hidden within such datasets.

Traditional AI techniques used to analyze data merely seek to findcorrelations among existing data sets. Although useful in establishingpossible patterns, a person is still required to reason or infer themeaning behind the patterns. Better techniques would allow a computinginfrastructure to reason or infer possible relationships among aspectsof a data set and then allow for automated validation of suchinferences.

Others have applied varying reasoning techniques for scientific purposesas described in the paper titled “The Automation of Science”, King etal., Science 3 Apr. 2009: Vol. 324 no. 5923 pp. 85-89. King's disclosedrobot successfully applied reasoning techniques to make several newdiscoveries.

Known techniques focus on deriving facts that can be used for otherpurposes. For example, European patent application publication 1 672 535to Staron et al. titled “Distributed intelligent diagnostic scheme” fileDec. 8, 2005, describes using reasoning techniques to diagnose networkissues. Further, U.S. Pat. No. 5,706,406 to Pollock titled “Architecturefor an Artificial Agent that Reasons Defeasibly”, filed May 22, 1995,describes reasoning that can be used to control robots, assembly lines,or as an adviser for medical diagnosis. Another example includes U.S.Pat. No. 7,003,433 to Yemini et al. titled “Apparatus and Method forEvent Correlation and Problem Reporting”, filed Jan. 12, 2005. Yeminidescribes mapping symptoms to problems through matrix-based reasoningtechniques.

Others have attempted to provide inference services. For example, U.S.Pat. No. 7,191,163 to Herrera et al. titled “System and Method forProviding Inference Services”, filed Apr. 18, 2003, describes inferenceservices with respect to specified domains. Another example includesU.S. Pat. No. 7,447,667 to Gong et al. titled “Method and KnowledgeStructures for Reasoning about Concepts Relations, and Rules”, filedDec. 11, 2002. Still further, U.S. patent application publication2006/0168195 to Maturana et la. titled “Distributed IntelligentDiagnostic Scheme”, filed Dec. 15, 2006, describes using inference basedon logical reasoning to diagnose or reconfigure a system. Yet anotherexample includes U.S. patent application publication 2007/0136222 toHorvitz titled “Question and Answer Architecture for Reasoning andClarifying Intentions, Goals, and Needs from Contextual Clues andContent”, filed Dec. 9, 2005. Still another example includes U.S. patentapplication publication 2011/0153362 to Valin et al. titled “Method andMechanism for Identifying Protecting, Requesting, Assisting and ManagingInformation”, filed Dec. 17, 2009.

Unless the context dictates the contrary, all ranges set forth hereinshould be interpreted as being inclusive of their endpoints, andopen-ended ranges should be interpreted to include commerciallypractical values. Similarly, all lists of values should be considered asinclusive of intermediate values unless the context indicates thecontrary.

It has yet to be appreciated that inference or reasoning techniques canbe applied to a general computing infrastructure where theinfrastructure itself can function as a reasoning engine based oncollected ambient data. Further, the known approaches fail to appreciatethe value of providing multiple possible reasoning approaches inresponse to an inquiry or altering the environment to seek validation ofa possible conclusion reached via the reasoning approaches.

Thus, there is still a need for reasoning engines.

SUMMARY OF THE INVENTION

The inventive subject matter provides apparatus, systems and methods inwhich one can utilize a reasoning engine to generate a hypothesisrepresenting an assertion that one or more aspects of an environmentmight have a correlation. Reasoning engines include a data interfacethrough which the reasoning engine can acquire environment datarepresenting aspects of the environment. Aspects can include variousmodalities of data (e.g., visual, audible, tactical, etc.) or even databeyond the perception of humans. In some embodiments, data from theenvironment can be represented as a manageable data object capable ofbeing transported through a networking fabric and having one or moreattributes describing the object. Objects can also include recognized oridentified real-world object having one or more attributes. Thecollected data can be aggregated at or passed through one or moreinference engines configured to generate a hypothesis according tovarious reasoning techniques. When a hypothesis is formulated, thereasoning engine can utilize a validation module to determine validityof the hypothesis by influencing acquisition of the environment data.

Various objects, features, aspects and advantages of the inventivesubject matter will become more apparent from the following detaileddescription of preferred embodiments, along with the accompanyingdrawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a schematic of reasoning engine environment.

FIG. 2 is a schematic of a reasoning engine.

FIG. 3 is a schematic of data flow through a reasoning engine.

FIG. 4 is a prior art example of submission of an inquiry to a searchengine.

DETAILED DESCRIPTION

It should be noted that while the following description is drawn to acomputer/server based reasoning system, various alternativeconfigurations are also deemed suitable and may employ various computingdevices including servers, interfaces, systems, platforms, databases,agents, peers, engines, controllers, or other types of computing devicesoperating individually or collectively. One should appreciate thecomputing devices comprise a processor configured to execute softwareinstructions stored on a tangible, non-transitory computer readablestorage medium (e.g., hard drive, solid state drive, RAM, flash, ROM,etc.). The software instructions preferably configure the computingdevice to provide the roles, responsibilities, or other functionality asdiscussed below with respect to the disclosed apparatus. In especiallypreferred embodiments, the various servers, systems, databases, orinterfaces exchange data using standardized protocols or algorithms,possibly based on HTTP, HTTPS, AES, public-private key exchanges, webservice APIs, known financial transaction protocols, or other electronicinformation exchanging methods. Data exchanges preferably are conductedover a packet-switched network, the Internet, LAN, WAN, VPN, or othertype of packet switched network.

One should appreciate that the disclosed techniques provide manyadvantageous technical effects including providing a computinginfrastructure capable of observing a data space and inferring possiblerelationships among data objects in the data space. When a hypothesis isformulated, the infrastructure generates one or more signals that canaffect collection of data in the data space. For example, a reasoningengine can observe a person's behavior and generate a hypothesisregarding the person's preferences. The engine can then generate ortransmit a signal to a device local to the person where the signalconfigures the device to collect additional data to determine thevalidity of the hypothesis.

As used herein, and unless the context dictates otherwise, the term“coupled to” is intended to include both direct coupling (in which twoelements that are coupled to each other contact each other) and indirectcoupling (in which at least one additional element is located betweenthe two elements). Therefore, the terms “coupled to” and “coupled with”are used synonymously. Within the context of this document, the terms“coupled to” and “coupled with” are also used to mean “communicativelycoupled with” over a network, possibly via one or more intermediarynetwork devices (e.g., peers, switches, routers, hubs, etc.).

FIG. 1 presents possible reasoning engine ecosystem 100 within thecontext of mobile phone interfacing with the reasoning engine 160 incloud 115. Mobile phones, or other sensors 120A through 120B, capturedata associated with one or more of environment 110. Inference engines140 acquire the data and apply one or more reasoning techniques in anattempt to discover possible correlations that one would likely neverhave considered. Inference engine 140 can review the data, possibly inthe form of manageable objects, to generate a possible hypothesisrelating to how aspects of environment 110 interact or are related.

Reasoning engine 160 utilizes data interface 130 as an input/outputinterface to the data space represented by environment 110. Datainterface 130 can take on many different forms. One preferred type ofdata interface 130 is a mobile computing device outfitted with one ormore sensors 120B. The mobile device collects environment data relatingto environment 110 local to the device. Environment data can includeuser identification, device usage metrics or data, sensor data, or othertypes of data representing aspects of the environment. Example mobiledevices can include mobile phones, vehicles (e.g., trucks, cars,airplanes, spacecraft, satellites, etc.), portable computers, gamingsystems, computing tablets, device controllers, or other types of mobiledevices. Furthermore, data interface 130 can comprise non-mobile devicespossibly including fixed sensors 120A, routers, switches, sensor webs,an appliance, a garment, or other devices capable of capturing data ofinterest.

Sensors 120A through 120B, cloud 115, and other items within ecosystem100 can communicate among each other as desired. In more preferredembodiments, elements within ecosystem communicate over one or morewireless protocols, depending on the nature of the element. For example,the major components in the system can utilize high bandwidth protocolspossibly including Ultra Wide Band (UWB), Wi-Fi, WiGIG, Multimodalbands, cellular networks (e.g., CDMA, GSM, etc.), or other types ofbroadband communications. Smaller elements, sensors 120A through 120Bfor example, can use lower bandwidth protocols including Zigbee, Wi-Fi,wireless USB, Bluetooth, near field communications, or other protocols.The protocols used for exchanging data among elements of ecosystem 100,especially via data interface 130, can be configured to fit thebandwidth needs of the communication channel.

One should appreciate that data interface 130 allows animate orinanimate objects to operate as I/O sources for reasoning engine 160.Animate objects typically include living animals, humans for example,but could also include non-living objects. Preferably animate objectsinclude human beings participating with aspects of environment 110 whileother animate living objects can include pets, animals, plants, or otherliving things. Example non-living animate objects include robots,vehicles, assembly lines, toys, or other items that can move. Inanimateobjects are consider to be objects that do not substantially change ormove with time including buildings, geological features, books,appliances, or other types of objects. Still further, data interface 130can be configured to acquire data associated with virtual objects oraugmented reality objects. In such cases, data interface 130 can operateas an API capable of providing access to information related to thevirtual objects, which allows such virtual objects to operate as I/Ointerfaces to reasoning engine 160.

Environment data can be obtained according to various modalitiesincluding modalities beyond human perception. Human modalities includesensor data representative of the human senses: audible data, visualdata, kinesthetic data, olfactory data, or taste data. Non-humanmodalities comprise a full spectrum of data beyond the human experienceand possibly include experimental data, radio frequency data, geologicdata, biometric data, medical or genomic data, accelerometer data,magnetometer data, electromagnetic spectrum outside visible light, orother types of data that a human can not perceive directly. Still othertypes of environment data can also be acquired including search queries,news events, weather data (e.g., barometric data, temperature data, winddata pressure data, etc.), demographics, user identity data, networkusage data, or other data that can be captured or measured. Theenvironment data can also include data from different locations,including locations that are geographically separated. For example,network traffic data can be acquired from the National Lambda Rail (seeURL www.nlr.net) in the United States while medical data is acquired inIndia.

As environment data flows into ecosystem 100, the environment data canbe processed to generate or otherwise create analyzed aspects of theenvironment. An analyzed aspect represents an object comprising aprocessed portion of the environment data that is not necessarilypresent in the environment per se. For example, a trend in a bodytemperature or an outdoor temperature can be considered an analyzedaspect of the environment. Analyzed aspects can be bound via objectattributes to parts of the environment, possibly bound to data interface130, a cell phone, a sensor, a user, a patient, a time, a location, orother elements of the environment for example.

Especially preferred analyzed aspects of environment 110 include thoserepresenting observed changes in the environment, which can also beconverted to a data object that can be analyzed or recognized. Suchobserved changes, or “deltas”, in observed environment metrics havepractical applications with respect to medical reasoning engines amongother types of reasoning applications. For example, data input intoecosystem 100 can include changes in characteristics of a biometricsensor with respect one or more sensor characteristics. The changes inthe characteristics can be indicative of a change in a patient's bodypossibly including pressure points, airway patency, respiration, bloodpressure, perspiration, body fluids, perfusion, or other types of bodychanges. These changes can be recognized by reasoning engine 160 andinfluence selection of reasoning rules, which then influences generatedhypotheses.

Sensors 120A through 120B can be consider inanimate objects as discussedabove, which can be observed by reasoning engine 160. Therefore changesin characteristics of such inanimate objects can be considered aspectsof environment 110. Characteristics of such objects can vary dependingon how the objects are observed via the output of sensors 120A through120B. Example characteristics that can be observed from sensors 120Athrough 120B can include physical characteristics (e.g., size, shape,dimension, mass, weight, material, etc.), electrical characteristics(e.g., resistance, current, voltage, capacitance, reactance, inductance,permeability, permittivity, etc.), mechanical characteristics (e.g.,stress, strain, torque, torsion, force, tension, compression, density,hardness, shear, friction, etc.), acoustical characteristics (e.g.,spectrum, frequency, wavelength, absorption, amplitude, phase, etc.),chemical characteristics (e.g., reactivity, pH, surface tension, surfaceenergy, etc.), optical characteristics (e.g., absorptivity, color,luminosity, photosensitivity, reflectivity, refractive index,transmittance, spectrum, spectrum, frequency, wavelength, absorption,amplitude, phase, polarization, etc.), or other types of properties.Such properties can be obtained directly from sensors 120A through 120Bor indirectly, and reflect changes in aspects of environment 110, apatients body or a building for example.

Environment data can be encapsulated within a data object, possibly inthe form of a serialized data structure (e.g., XML, WSDL, SOAP, etc.).Data objects reflect some aspect of an environment as a quantized unithaving one or more object attributes describing the data. For example,an image acquired via a mobile phone can be treated as a data objectwith attributes describing the image (e.g., resolution, data size, colorbalance, etc.) or metadata (e.g., camera used, photographer identity,time stamps, orientation, location, etc.). In some embodiments, theattributes of the objects are assigned according to a normalizednamespace allowing various objects to be easily compared with eachother.

Reasoning engine 160 can utilize one or more namespaces when analyzingrecognized objects with respect to each other. Some embodiments canstore object attributes according to different normalized namespaceswhere each object can be represented in different fashions. For example,when an object is recognized in an image as a piece of fruit, theobject's attributes can be provisioned as a function of two or morenormalized namespace. A first namespace might be fruit-specific (e.g.,apples, organs, bananas, etc.) where the fruit-specific namespaceadheres to a fruit-specific ontology. A second normalized namespacemight relate to nutrition and adhere to a nutrition ontology.

One should appreciate that a data object can be more than a file orquanta of data. More preferred data objects can include digitalrepresentations of real-world objects or concepts (e.g., people, things,symbols, emotions, etc.) found in a scene in environment 110. Objectscan be recognized by features derived from the digital representation(e.g., image, video, audio, sensor data, etc.) captured by sensors 120Aor 120B. When an object is recognized or identified, its features (e.g.,attributes, properties, characteristics, etc.) can be auto-populated.Suitable techniques for object recognition that can be adapted for useare described in U.S. Pat. Nos. 7,016,532; 7,477,780; 7,680,324;7,565,008; and 7,564,469. Furthermore, objects can also exist in anaugmented reality or even a non-human data space (e.g., networkinginfrastructure management system, robotic environment (seewww.roboearth.org), data analysis engine, etc.). For example, AugmentedReality (AR) object 112 might only exist within data interface 130, butcould still be considered a virtual part of environment 110. Thus,recognized objects in environment 110 can include real-world objects,virtual objects, or other types of objects in the data space.

Inference engines (IE) 140 include one or more computing devicessuitability configured to apply programmatic reasoning techniques,individually or in aggregate, to the acquired environment data. One mainaspect of the inventive subject matter is to discover possiblecorrelations among aspects (e.g., objects, items, concepts, contexts,reasoned facts, etc.) of environment 110 where the correlations caninclude ones that a person would not ordinarily expect to consider.Inference engines 140 observe the environment data and attempt toestablish one or more hypotheses representing a possible relationship orcorrelation between aspects of the environment. An example expert systemthat could be suitably adapted for use as an inference engine includesthe system described in international patent application WO 88/05574 toWolf titled “Expert Knowledge System Development Tool”, filed Jan. 20,1987.

Various reasoning techniques can be applied to determine if “reasoned”connections might exist. Logically consistent reasoning techniques caninclude deductive reasoning, case-based reasoning, or other types ofreasoning having consistent boundaries. More preferred reasoningtechniques allow inference engines 140 to make a leap beyond conclusionsthat have stringent necessary and sufficient conditions to conclusionsthat could lack sound premises; thus creating the opportunity fordiscovery or sparking creativity. Additional reasoning techniquesallowing for such a leap include abductive reasoning, indicativereasoning, fuzzy logic, or other types of reasoning that lack arequirement for necessary or even sufficient criteria. One shouldappreciate that a hybrid; for example applying deductive reasoning topremises generated from abductive reasoning to form interestinghypothesis. UK patent application publication GB 2 278 467 to Imlahtitled “A knowledge based system implementing abductive reasoning”,filed May 20, 1993, describes applications of abductive reasoningtechniques that can be suitably adapted for use with inventive subjectmatter.

Examples for different styles of reasoning can also include forwardchaining or backward chaining reasoning as disclosed in European patentapplication publication 0 453 874 to Highland et al. titled “A computerbased inference engine device and method thereof for integratingbackward chaining and forward chaining reasoning”, filed Apr. 10, 1991.Another example, includes U.S. Pat. No. 7,418,434 to Barry titled“Forward-Chaining Inferencing”, filed May 13, 2005. The disclosedtechniques could be suitably adapted for use with the inventive subjectmatter. Examples of case-based reasoning are discussed in U.S. Pat. No.5,581,664 to Allen et al. titled “Case-based Reasoning System”, filedMay 23, 1994.

Another technique capable of generating hypotheses includes metaphoricalmapping. Metaphorical mapping comprises establishing similaritiesbetween known concepts and a current concept under evaluation. A knownconcept can be represented by relationships among known objects (e.g.,data object, sensed objects, recognized objects, recognized concepts,etc.) where each of the objects or relationships can be characterized byattributes, possibly arranged according to one or more concept maps.U.S. patent application 2010/0287478 to Avasarala et al. titled“Semi-Automated and Inter-active System and Method for Analyzing PatentLandscapes”, filed May 11, 2009, describes techniques for building andcomparing concept maps in a patent landscape. Such techniques can bereadily adapted for use herein where concept maps are built according torecognized aspects in environment 110. Inference engine 140 can use oneor more of the previously discussed reasoning technique to generate apossible similarity between the features of a current concept to thefeatures of known concepts or ontologies. If inference engine 140determines a sufficient similarity exists via a satisfaction criteria,then the known concept can be considered a metaphor for the currentconcept. Each known concept map can be considered a manageable objectand can comprise extrapolation paths representing a algorithmicstructure through which a metaphor can be extended. A hypothesis canthen be drawn by extending the current concept along extrapolation pathsaccording to the known context. One example of generating hypothesesthat could be suitably adapted for use includes those described in U.S.published patent application 2011/0231356 to Vaidyanathan et al. titled“Flexscape: Data Driven Hypothesis Testing and Generation Systems”,filed Jul. 1, 2010.

With respect to metaphorical mapping, consider an example scenario wherereasoning engine 160 identifies a round red object, perhaps a playgroundball. Inference engine 140 generates a concept map associated with theball. Based on the structure of the ball's concept map, inference engine140 might identify that an apple concept map might be a reasonablemetaphor for the ball. The apple concept map can include extrapolationpaths that could include concepts relating to growing, harvesting,eating, nutrition, varieties, or other concepts related to apples. Theseextrapolation paths can then be applied to the ball concept and mightyield a hypothesis that balls can be harvested, or have differentvarieties. Although the hypotheses might seem nonsensical at firstreview, they can spark additional cognitive reasoning or conclusionsthat can have value for creativity, imaginative, or emotional purposes.For example, harvesting balls might metaphorically map to picking upballs after a recess in a school.

Inference engines 140 can also take on many different forms. Examplesinclude dedicated computing devices; a server or search engine forexample. Inference engines 140 can also be deployed within datainterface 130, a mobile phone for example, when quick localizedreasoning would be beneficial. Yet further examples including networkingnodes operating as inference engines 140 and also operate as a generaldata transport. For example, inference engines can be deployed within aswitch, a router, a web server, a gateway, an access point, a firewall,or other type of network device. For example, the nodes of the NationalLamba Rail can function as an inference engine operating on medical orgenomic data flowing through the network where the medical data can beconsidered a non-human modality associated with a medical data space.

It is specifically contemplated that inference engine 140 can comprise amember of a distributed reasoning engines 160 as illustrated wherevarious elements exchange hypotheses among each other. In such anembodiment, the environment data collected by inference engines 140 canalso include previously generated hypotheses. Thus, reasoning engines160 are able to build on and learn from previous experiences orpreviously reasoned facts; even reasoned facts that are not necessarilytrue. One should appreciate that such reflexive reasoning allows thereasoning engine to observe itself, creating a reasoning enginereflexive data space. One aspect of the inventive subject matter isconsidered to include management of such reflexive reasoning to ensurethe behavior converges or diverges appropriately. Reflexive managementcan include tracking number of iterations through a reasoning loop,number of nested hypotheses, uncertainty score or merit scores ofhypotheses, or other metrics. Should the metrics satisfy a reflexiveobservation criteria, the reflexive reasoning can be haltedappropriately. Uncertainty in a reasoning sequence can be based ontechniques disclosed in U.S. Pat. No. 4,860,213 to Bonissone titled“Reasoning System for Reasoning with Uncertainty”, filed Oct. 1, 1987.Bonissone describes propagating uncertainty information through areasoning based system. Such an approach can be further adapted forassigning merit scores or rankings to hypotheses.

An astute reader will appreciate that a formulated hypothesis does notnecessarily equate to a true hypothesis in view of the reasoningtechniques. To establish validity, at least at some level, of ahypothesis, a reasoning engine can further comprise one or morevalidation modules (VM) 150. Validation module 150 can be configured torun one or more experiments on the data space (i.e., environment 110) inan attempt to validate the hypothesis through controlling acquisition ofthe environment data. For example, inference engine 140 mighthypothesize that a person is always at a specific location at a time ofday based on the person's cell phone location. Then validation module150 can then instruct the cell phone (e.g., data interface 120) toacquire location data, possibly GPS data, at the specified time. If theacquired data matches the hypothesis, the hypothesis is likely valid, ora merit score of the hypothesis can be adjusted appropriately.

Validity of a hypothesis can be determined with respect to one or morecriteria sets, possibly according to differing dimensions of relevance.In the previous example, validity was determined by empirical data.Rather than experimentally obtaining validation data, the validationmodule could also seek validity through subjective data by constructinga survey targeting a demographic. Such an approach is consideredadvantageous when determining when a demographic might be interested ina hypothesis representing an imaginative construct, a creative work, oreven an emotion.

One should appreciate the difference between seeking feedback from auser versus empirical evidence from the data space to validate ahypothesis. Seeking feedback might include submitting a question back toa user where the user validates the hypothesis. Empirical evidenceincludes controlling acquisition of the environment data according to avalidation plan. The validation plan could include instructions from oneor more of the validation modules 150 to generate commands, which aresubmitted to data interface 130. The submitted commands can controlacquisition of data from sensors 120A through 120B. Further, validationmodules 150 can inject data back into environment 110 according to thevalidation plan to observe changes in the environment 110.

Hypotheses are considered dynamic objects and in some cases can changewith time. Inference engine 140 can continually generate hypothesesbased on information flow from sensors 120A through 120B or otherinformation relating to environment 110. Some hypotheses can begenerated in real-time based on the ever changing nature of environment110, while other hypotheses might be generated over extended periods oftime once a suitable number of observations have been acquired. Forexample, reasoning engine 160 and its components can store a hypothesisobject in their corresponding memories. As additional data is madeavailable, the hypothesis object can be modified in real-time; at thespeed of memory accesses (e.g., Flash, RAM, hard disk, solid state disk,etc.). Thus, a user can observe changes to a hypothesis as it evolvesover time.

In some scenarios a hypothesis has a temporal nature where thehypothesis's suspected correlation has time-based values. For example, ahypothesis might indicate a person's preference for apples. However, thevalidity of the hypothesis might vary throughout a day, week, month, oryear. A hypothesis merit score or validity score can change with time asnew data becomes available.

One of the aspects of the inventive subject matter is considered toinclude establishing validity according to one or more dimensions ofrelevance. Consider a case where inference engine 140 generates ahypothesis that a segment of the population would be interested in atopic; a movie or game for example. Validation module 150 can recognizethere are many dimensions of relevance with respect to the populationpossibly including age or gender, which represent just two dimensions ofrelevance. To validate the concept, validation modules 150 can create asurvey and present the survey only to individuals according to age orgender, or other dimensions of relevance. Validation module 150 mightdiscern, or otherwise learn, that the hypothesis is valid for one genderbut not another. Such an approach can be extended to many types ofhypotheses. All dimensions of relevance are contemplated.

Although the above discussion is presented generically, there are manyarenas where such reasoning engines can be used. As inference engine 140operates to generate or validate hypotheses, they effectively learnabout external environments 110. One major distinction relative toprevious AI techniques is the disclosed reasoning engines can directly,or indirectly, interact with the environments 110 to learn if thehypotheses are valid or in valid. Furthermore, reasoning engines 160 canoperate regardless of the type of environment being evaluated. Exampleenvironments can include a global environment, a real-world environment,a virtual environment (e.g., within a game world), an augmented realitymixing real-world and virtual world objects, a data space environment(e.g., non-human space), or other types of environments 110. An exampledata space environment could include network infrastructure managementwhere network management data is exchanged among networking nodes.Reasoning engine 160 can operate as a network management functionalitydeployed within one of the nodes.

A hypothesis is considered to represent more than a static correlationbetween or among data points. A hypothesis can also represent humanexperiences as briefly referenced above. For example a hypothesis canrepresent an intuition, an imaginative construct, a creative work, anemotion, or other type of traditional human experience. Considerintuition for example. A reasoning engine 160 can include multiplelayers of inference engines where a sub-engine generates a hypothesis,and might even validate the hypothesis to some degrees. The sub-enginesends the hypothesis up to a next layer as an object where the nextlayer uses the hypothesis as input for its evaluations. Thus, thesub-engine's hypothesis can be considered intuition percolating upthrough the reasoning engine. Another suitable technique that can beadapted for use for establishing intuition includes those described byU.S. Pat. No. 5,175,795 to Tsuda et al. titled “Hybridized FrameInference and Fuzzy Reasoning System and Methods”, filed Jul. 13, 1989.Emotional hypothesis could be as simple as differentiating betweenaspects of the environment (e.g., sounds, sights, facial expressions,objects, etc.) and generating a hypothesis relating reactions ofindividuals relative to observed aspects of the environment (see FIG. 3). One can consider reasoning engine 160 to be emotionally aware of anenvironment based on the type or tenor of the captured data (e.g.,music, art, nature, tone, etc.) mapped to appropriate emotional conceptmaps.

In view that reasoning engine 160 can also interact with an environment,reasoning engine 160 can also inject data back into the environmentbased on the hypothesis, preferably according to a validation plan. Theinjected data can be presented through one or more presentation modules.In some embodiments, the injected data can represents surveys asdiscussed above, advertisements, recommendations, commands, or othertypes of data that affect the environment.

Although the above discussion is presented in broad brush strokes, thereare many practical applications. Example applications includingadvertising, network management, experimental research, contentcreation, innovation, or other areas where humans excel over machines.Still, it is expected that human interaction would be advantageous toselect validated hypotheses for actual application.

One interesting application can include providing individuals with apersonalized reasoning engine or inference engine where the personalizedinference engine operates according to dictated or learned preferencesof the individual. The personalized inference engine can retain accessto other inference engines if desired to gain from other experiences. Insome embodiments, the personalized inference engine can operate as areasoning companion that observes ambient data, possibly in real-time,surrounding an individual. The companion can reason that the individualmight be interested in a topic (i.e., a hypothesis) based on the ambientdata (e.g., idle conversations, images, etc.). To validate thehypothesis, the companion can present the topic to the individual,possibly in a non-obtrusive manner, or alter the environment and observethe individual's reaction. Through learning more about the individual byvalidating hypotheses, the reasoning companion becomes a strongercompanion capable of anticipating the individual's needs or desiresbased on a context derived from ambient data.

FIG. 2 presents a more detail view of a possible reasoning engine 260.Reasoning engine 260 can be implemented according to various techniques.In some embodiments, reasoning engine 260 operates as an Internet basedservice, possibly a for-fee service, in a similar fashion as a searchengine. For example, reasoning engine 260 could provide reasoningservices to users for free. As inquiries are submitted to reasoningengine 260, the reasoning engine can present advertisements along withhypothesized conclusions. Advertisers can pay for advertisements basedon reasoning techniques used for a topic. For the purposes ofdiscussion, reasoning engine 260 is presented as a server platform.However, one should appreciate that reasoning engine 260 can also bedeployed within a mobile device, a network switch, a car navigationsystem, or other computer-based device. It is especially contemplatedthat reasoning engine 160, or portions of reasoning engine 160, can bedistributed across multiple computing devices from an end user device(e.g., a cell phone) through networking infrastructure (e.g., switches,routers, ISPs, etc.), to cloud-based services (e.g., virtual servers,Amazon® EC2, etc.) where each portion communicates reasoning informationor objects using appropriate protocols (e.g., WSDL, SOAP, HTTP, TCP/IP,etc.).

With respect to network switches, example switches that could hostreasoning engine 260 could include those offered by Cisco® (e.g., CiscoCatalyst, Nexus, etc.) or Juniper® (e.g., EX4200, EX4500, etc.). It isconsidered advantageous to deploy reasoning engine 260 within suchinfrastructure because the networking infrastructure itself can functionas a computational fabric as acquired environment data flows from nodeto node. Switches capable of operating as a computational fabric thatcould operate as reasoning engine 260 are discussed in U.S. Pat. Nos.7,904,602; 7,548,556; 7,603,428; U.S. published application2010/0312913; and U.S. applications having Ser. Nos. 13/024,176 and13/024,240.

Reasoning engine 260 can include one or more of data interface 230. Inthe example shown, data interface 230 comprises an HTTP server throughwhich remote devices interact with reasoning engine 260. Preferably datainterface 230 is configured to acquire or receive sensor data or digitalrepresentations of a scene having one or more objects. For example, datainterface 230 could couple with a cell phone over the Internet or cellnetwork. Alternatively, data interface 230 could couple with otherinformation sources including weather stations, news or media outlets,stock markets, web sites or services, radio stations, or other sourcesof data.

Reasoning engine 260 preferably includes one or more of knowledge base265. Knowledge base 265 represents one or more databases storinginformation related to reasoning processes. In some embodiments, theknowledge stored in knowledge base 265 is personalized to a specificindividual. In such embodiments, a person's reasoning engine 260 mightfunction differently than another person's reasoning engine 260 evenwhen the same reasoning rules are applied. In other embodiments,knowledge base 265 can include information across a population ordemographic, or other across type of classifications (e.g., geographic,retailers, affiliations, etc.).

Knowledge base 265 can store a broad spectrum of objects in support ofreasoning engine 260. Example objects include known target objects thatreasoning engine 260 can recognize, ontologies describingdomain-specific information, reasoning rule sets, previously generatedhypotheses, reasoned knowledge or facts, suspected or establishedcorrelations, or other information. It should be appreciated that eachof these types of objects can be managed as distinct objects where eachtype of object comprises attributes describing the nature of the object.As discussed previously the object attributes can adhere to one or morenormalized or common namespaces, which not only allows for comparison ofone object to another in the same domain, but comparison of one objectto other types of objects even when the objects are acrossknowledge-specific domains. For example, a hypothesis generated withrespect to mapping an unknown word of a language to a recognized targetobject (e.g., an apple) could be compared to correlations derived fromanalysis of patents even though the two concepts are widely different.

Preferably reasoning engine 260 comprises one or more of inferenceengine 240. Inference engine 240 represents the component of theecosystem configured to derive interesting relationships amongrecognized objects associated with an inquiry. Inference engine 240 asillustrated is configured to receive an inquiry relating to one or moreaspects (e.g., symbols, objects, utterances, observations, etc.) of anenvironment. In some embodiments, the inquiry could be obtained directlyvia data inference 230 in the form of a digital representation of theenvironment or the aspects. It is also contemplated that the inquiry canbe obtained via other avenues as well. For example, the inquiry could besubmitted through a search engine, Application Program Interface (API),or even internally generated.

As the environment data associated with the aspects of the environmententers inference engine 240, it can be passed to recognition engine 242.Through recognition engine 242, inference engine 240 is configured torecognize the aspects of the environment as target objects 243, whereeach of the target objects 243 has object attributes as discussedpreviously. Although target objects 243 can represent items identifieddirectly from the environment data, one should further appreciate thattarget objects 243 can include generated target objects internal toinference engine 240: previously generated hypotheses or reasoned facts,suggested correlations, validation plans, or other objects.

Recognition engine 242 can recognize the aspects of the environment asthe target objects 243 through various techniques, or even combinedtechniques. In some embodiments, recognition engine 242 analyzes theenvironment data to derived one or more data features where the datafeatures represents characteristics of the data itself in addition tothe features of the data content (e.g., objects, sounds, symbols, etc.)associated with items in the scene. Example imaged-based data featurescould include those derived from Scale Invariant Feature Transform(SIFT; U.S. Pat. No. 6,711,293), Virtual Simultaneous Localization andMapping (vSLAM™; “The vSLAM Algorithm for Robust Localization andMapping”, Karlsson et al., Proc. of Int. Conf. on Robotics andAutomation (ICRA) 2005), Speeded Up Robust Feature (SURF), VisualPattern Recognition (ViPR), or other data features. The values generatedfrom such features can be used as an index into a knowledge base toretrieve known target objects 243 having similar features. Thus,recognition engine 242 can recognize aspects of the environment astarget objects 243. Other types of data features beyond visual modalitycan also be used using known techniques for each type of modality.

One should appreciate that recognition engine 252 can also operate inreverse. When aspects of the environment do not map to known targetobjects 243, then recognition engine 242 can instantiate a target object243 as a newly discovered object in the knowledge base where the objectattributes are provisioned from the information available from theenvironment data. For example, a user might wish to discoverrelationships associated with an apple. The user images an apple, whichis not known for the sake of argument. Recognition engine 242 identifiesthat there is an unknown object in the environment data. Inferenceengine 240 supplies additional information associated with the unknownobject (e.g., color, size, position, orientation, location, etc.), whichis then used to provision the attributes of the object. The object canthen be stored in the knowledge base. Based on additional information,inference engine 240 might at a later date bind more meaningful data tothe object (see FIG. 3 ); that the unknown object is a fruit called anapple.

Inference engine 240 can also include one or more of rule selector 244.Rule selector 244 configures inference engine 240 to select one or moreof reasoning rules set 245 from knowledge base 265 as a function of theenvironment data and object attributes of the recognized target objects243. Rule selector 244 can select desired reasoning rules sets 245 viaone or more selection criteria. In some embodiments, rule selector 244maps target object attributes to known concept maps, which can includepointers to preferred types of reasoning: deductive, abductive,inductive, ampliative reasoning, case-based reasoning, metaphoricalmapping, forward chaining, backward chaining, analogical reasoning,cause-and-effect reasoning, defeasible or indefeasible reasoning,comparative reasoning, conditional reasoning, decompositional reasoning,exemplar reasoning, modal logic, set-base reasoning, systemic reasoning,syllogistic reasoning, probabilistic reasoning, paraconsistentreasoning, or other types of reasoning.

One should appreciate the rule selector 244 could vary across a broadspectrum of capabilities from one inference engine 240 to another basedon the configuration of inference engine 240. In embodiments wherereasoning engine 260 as been adapted for personal use, rule selector 244can be configured to weight selection of a non-logical reasoning overlogical reasoning because the user wishes to discover relationships thatmight be relevant to a work of art or creativity. While another person'sreasoning engine 240 might weight selection of cause-and-effectreasoning (e.g., cause-to-effect reasoning, effects-to-cause reasoning,the Bradford Hill Criteria, etc.) because more rigorous reasoning isrequired, possibly for use in medical diagnosis or scientific research.Thus, hypotheses 246 generated from the selected reasoning rules set 245could vary widely even if target objects 243 are the same from one userto another.

Inference engine 240 is preferably configured to establish one or moreof hypothesis 246 according to the one or more selected reasoning rulesset 245. Hypothesis 246 represents a suspected correlation that mightexist among target objects 243. As discussed previously, the suspectedcorrelation does not necessarily have to be valid or even true. Ratherhypothesis 246 seeks to fulfill the goal of an incoming inquiry, whichis to seek a possible relationship based on recognized aspects of theenvironment. Hypothesis 246 can be presented via one or more ofpresentation module 270 back to the inquirer.

In some embodiments, inference engine 240 comprises at least one ofvalidation module 250 coupled with inference engine 240. Validationmodule 250 is preferably configured to construct one or more ofvalidation plan 255, which outlines a path to establish validity ofhypothesis 246. Validation plan 255 can be derived from the suspectedcorrelation embodied by hypothesis 246. Information from the suspectedcorrelation can include types of environment data that can be used tofurther corroborate the correlation. Based on validation plan 255,validation module 250 can be configured to request updated environmentdata with respect to the aspects of the environment via data interface230. Validation plan 255 can include a direct query to a user to seekvalidation. However, a more preferred approach includes validationmodule 250 controlling acquisition of the environment data according tovalidation plan 255 without user involvement. In some embodiments,validation module 250 submits sensor commands to data interface 230,which in turn controls how, when, which, where, what or other aspect ofsensor data collection. In a very real sense, validation module 250conducts an experiment on the target data space to see if the suspectedcorrelation holds. Thus, validation module 250 can inject data back intothe environment via presentation module 270 and data interface 230.

Validation module 250 is further configured to generate a derivedrelationship among the aspects of the environment as a function of thevalidation plan 255 and the updated environment data. The derivedrelationships do not necessarily have to be a binary true or false, butcan include a spectrum of values. As statistics are built up over time,the derived relationships cause an increase or decrease a merit scoreassociated with hypothesis 246.

FIG. 3 illustrates a more specific example where reasoning engine 360 isutilized as a form of Rosetta stone where reasoning engine 360 collectsinformation associated with an object, an apple in this case, accordingto different languages, English in this case. Although the followingdiscussion references the English language, one should appreciate thatthe techniques are language independent. Therefore, reasoning engine 360can infer conceptual information associated with a recognized object ina language independent fashion.

In the example shown, reasoning engine 360 receives environment datarelated to environmental aspect 305A and aspect 305B. Aspect 305Arepresents imaged data (e.g., visual modality) related to an apple andaspect 305B represent audio data (e.g., auditory modality) for anutterance. The environment data can be processed or pre-processed by theacquiring device (e.g., cell phone, sensor, etc.), an intermediarydevice (e.g., search engine, etc), or reasoning engine 360 as desired.Reasoning engine 360 receives the environment data associated withaspects 305A and 305B and attempts to recognize the aspects as targetobjects. The environment data can be collected with intent, where a usersubmits the information, possibly via inquiry 380, or without intent.For example, reasoning engine 360 can be configured to collect ambientdata as data flows through a sensor, gateway, network switch, cellphone, or search engine where ambient data triggers formation of inquiry380.

Reasoning engine 360 recognizes aspects 305A and 305B as target object334A and 334B respectively. Target object 334A is recognized as having aset of object attributes including size, shape, color, name, or otherinformation related to aspects 305A. Further, target object 334B isrecognized as an utterance relating to fruit. In the example shown,reasoning engine 360 is able to establish that target objects 334A and334B are subject to inquiry 380 through various techniques. In onescenario, inquiry 380 specifically references aspects 305A and 305B. Inother scenarios, aspects 305A and 305B might be related to because ofrelative proximity to each other in location, in time, with respect tothe user, or other attribute.

Inquiry 380 can take on many different forms. As discussed previously,inquiry 380 can be an explicit inquiry submitted by a user or could bean auto generated inquiry generated by reasoning engine 360 or otherdevice. In some embodiments, inquiry 380 can be auto generated as partof a validation plan. Thus, inquiry 380 can comprise humanunderstandable modalities, machine understandable modalities, or othertypes of modalities. In the example shown, inquiry 380 as translated issubmitted to reasoning engine as “How could aspects A and B berelated?”, which causes reasoning engine 360 to begin an analysis.

The term “inquiry” is differentiated from the term “query” within thecontext of this document. A query is considered to be a request forexisting information that relates to the queried subject matter. A queryis keyword-based where a search engine or other type of database returnsa result set of existing documents or information that match keywords inthe query. An inquiry is considered to be a request for augmentingknowledge, resolving doubt, or solving a problem rather than mere a setof keywords. With respect to the example shown, inquiry 380 is aninquiry because the request relates to possible relationships that mayor may not exist, or have not yet even been considered previously.Inquiry 380 is not considered a query because the request does notnecessarily relate to a keyword-base search for existing documents.

Reasoning engine 360 accepts inquiry 380 and selects one or morereasoning rules sets, in this case multiple rule sets are selected foruse to establish hypotheses 346 to explore the possible relationshipsamong target objects 334A and 334B. Hypotheses 346 represents a set ofpossible inferences generated by reasoning engine 360 for the specificsituation where the hypotheses 346 are ranked according to some criteriaas discussed with respect to presentations 347. Hypotheses 346, again,do not necessarily have to be true or valid. Rather, hypotheses 346represent an exploration of the conceptual space or spaces associatedwith target objects 334A and 334B.

For the current example, reasoning engine 340 hypothesizes that theEnglish word “Fruit” applies to an apple object. Assuming that validityof such a hypothesis survives validation, to within a validationcriteria, reasoning engine 340 can update attributes of target object334A to reflect that target object 334A is a “Fruit” as least inEnglish. Thus the reasoning engine 360 can continually inferrelationships among words in any language and target object 334A and canauto populate target object 334A with language-specific meaning. Recallthat target objects 334A and 334B can have attributes that adhere todifferent ontologies or namespaces. Therefore, reasoning engine 360 canmap the language-specific information to a proper namespace based on thelanguage used or the concepts derived from the environment data. Throughsuch a mapping reasoning engine 360 can operate as a Rosetta stone amonglanguages built on inferences generated from recognized objects orconcepts.

Hypotheses 346 are presented as presentations 347, which could berendered within a browser on a user's computing device as illustrated.However, presentations 347 could also be feedback into reasoning engine360 as new objects for further analysis or convergence on additionalhypotheses. In the example shown, presentations 347 renders hypotheses346 in a ranked manner according to a merit score.

The merit scores can be a single valued measure of validity for eachhypothesis as illustrated. The validity merit score can be derived basedon the type of reasoning used to generate the hypothesis. For example, ahypothesis reasoned according to deductive reasoning (see hypothesis 1of hypotheses 346) likely has a higher validity than a hypothesisreasoned through inductive reasoning (see hypothesis 3 of hypotheses346). As reasoning engine 360 develops confidence that a hypothesis isvalid, the merit score can change appropriately. In such scenarios, therankings in presentations 347 can change with time.

The merit scores presented in presentations 347 are illustrated assingle valued scores, or as having a single dimension of relevance. Oneshould appreciate that merit scores can be multi-valued where each valuerepresents a different dimension of relevance. For example, validitymight have value for medical diagnosis, but lacks relevance with respectto an imaginative, creative, or emotional inference. Thus, eachhypothesis could include numerous merit scores that are valued as afunction of how the various reasoning rules sets map to each type ofinference.

In view that hypotheses 346 are not necessarily true or valid based onthe type of inference that is desired to be used, reasoning engine 360can seek one or more of validations 355. In the example shown,validations 355 are generated by a validation module and are directed toseek explicit validation from a user for a hypothesis. In this case, thevalidation module determines that “All green things are fruits” is not avalid hypothesis. Although reasoning engine 360 can employ explicitvalidation, reasoning engine 360 can also seek indirect or empiricalvalidation through conducting an experiment. For example, the validationmodule can be configured to submit a validation request to another user,possibly in a different language. The submitted request could be of theform “Would you like a piece of fruit, an apple perhaps?”. Note therequest does not seek explicit validation, rather reasoning engine 360uses its inferred hypothesis as practical knowledge to see if thehypothesis holds based on how an entity in the data space reacts.

Presentations 347 include additional information beyond hypotheses 346and their associated merit scores. Preferably, reasoning engine 360 alsopresents how hypotheses 346 were reasoned, including the reasoning stepstaken. Such an approach is considered advantageous because the user(e.g., person, machine, etc.) can obtain a better understanding of the“creative” process behind the assertion, which can trigger additionalcreative thoughts in the user.

Consider a real-world situation. The question “How are ravens like awriting desk?” was submitted to WolframAlpha.com (see FIG. 4 ). Such aquestion is nonsensical and was intended to be so. WolframAlpha™ returnsa query-based response “Because Poe wrote on both” based on retrieval ofdocuments, but does not return inferred reasoning on how a raven couldindeed be like a writing desk. In the disclosed approach, the user wouldbe presented with one or more hypotheses along with reasoning behind thehypothesis. It is also contemplated that a hypothesis of NULL could begenerated by the disclosed approach (i.e., “there does not appear to bea reason why ravens are like a writing desk”). The disclosed approachallows for expanding the reasoning, creativity, imagination, or evenemotional awareness of the user especially in view that the hypothesiscan be considered creative works, imaginative constructs, or evenemotions.

The inventive subject matter can be applied across a wide variety ofmarkets including commercial markets, educational markets, entertainmentmarkets, medical markets, or other types of markets. Consider medicalreasoning for example. A reasoning engine as disclosed can be configuredto operate within a medical data space. For example, a medical reasoningengine can observe how healthcare providers (e.g., doctors, surgeons,pharmacists, etc.) treat patients and observe results obtained from thetreatment protocols applied to the patients. In such a scenario, thereasoning engine can view the treatment protocols and results as aspectsof the medical data space environment. Further, reasoning engine cangenerate one or more hypotheses relating to the positive, or negative,outcome from the treatment protocols. In such embodiments, it isunlikely that the reasoning engine will alter the behavior of thetreatment protocol. However, the reasoning engine can offer thehypotheses as recommendations on changes to the treatment protocol.

The medical hypotheses can be validated, to some extent, by thereasoning engine through the reasoning engine seeking additional dataacross the medical data space according to a validation plan. Forexample, the medical reasoning engine might hypothesize that a group ofpatients belonging to a specific demographic appears to respond to aspecific treatment protocol or surgery. The reasoning engine can thencapture sensor data from other patients within similar demographics, oreven dissimilar demographics, in a non-intrusive fashion and then usethe sensor data to determine if the hypothesis is supported or notsupported. Based on the validation process, the reasoning engine canalter its recommendations. Such techniques can be applied to surgeries,prescriptions, chemotherapy, blood work analysis, family history review,or other types of medical related activities.

The disclosed reasoning engines can also be applied to commercialreasoning. Commercial reasoning engine have applicability to bothsellers and buyers. From the seller's perspective, a vendor or othertype of retailer can leverage the services of a reasoning engine in anattempt to figure out how or why revenues change with time. The vendorcan supply vendor information into the reasoning engine from varioussources. The vendor information could include sales figures, promotions,employees, coupons, planograms, customer demographics, or otherinformation. The vendor's reasoning engine can then offer hypothesesabout how aspects of the business directly or indirectly relate tobusiness performance metrics. The vendor's reasoning engine can furtheroffer recommendations (i.e., a validation plan) on altering the businessto confirm or invalidate the hypotheses. From a buyer's perspective, abuyer can also utilize a personal reasoning engine configure to observethe buyer's behaviors and hypothesize about the buyer's preferences. Asthe reasoning engine evolves and learns about the buyer, the reasoningengine can generate recommendations as part of a validation plan basedon the hypothesized preferences. As data from a goods and services dataspace flows into the buyer's reasoning engine, the engine can infer thatthe buyer might be interested in newly presented promotions (e.g.,advertised goods and services, coupons, deals, sales, etc.). The buyercan then be enabled to purchase the products (e.g., movie tickets,music, videos, games, products, etc.) according to the promotions. Insome embodiments, hypotheses are validated through auto construction ofa promotion according to the validation plan. More specifically, apromotion or coupon could correspond to a validation activity.Regardless of the vendor's or buyer's perspective, the reasoning enginecan conduct its inference analysis based on the attributes of theentities involved (e.g., buyer, seller, consumers, etc.) as well asproduct features.

Another interesting application of the disclosed techniques includesapplying reasoning engines for educational purposes. Students canleverage reasoning engines in different manners depending on the lessonsto be learned. In some embodiments, the reasoning engines can illustrateto a student how to assess incoming information and derive meaningfulknowledge from the information. For example, when working in math orscience, the reasoning engine can show the student how deductivereasoning is applied and how it can provide necessary and sufficientconditions for a true result: If A=B and B=C, then A=C for example.Alternatively, educational reasoning engines can be configured togenerate hypotheses lacking sufficient conditions for validity as achallenge to the student to determine the validity of the hypothesis.Still further, educational reasoning engines can be configured to reasonin a manner that is age-appropriate for the student, or appropriate forthe student's capabilities. A young student might only be able toprocess simple reasoning rules (e.g., deductive reasoning) while anolder student might be able to process more complex reasoning rules(e.g., abductive, hybrid reasoning techniques, forward-chaining, etc.).The student's reasoning engine can then evolve with the student.

As discussed previously reasoning engines can be applied to translation.A translation reasoning engine can generate hypotheses that represent asuspected correlation that a word in a language, or languages,corresponds to one or more conceptual objects. As a Rosetta stone styletranslation reasoning engine continues operate based on informationobtained, its capabilities in translating concepts from one language toanother improve. Further, translation reasoning engines can provideusers with hypotheses indicating possible translations obtained fromforeign languages. For example, a user might travel from the UnitedStates to China. As the user interacts with one or more Chinese persons,the English speaker can use their cell phone operating as a translationreasoning engine to present hypotheses representing the conceptualmeaning being discussed in Chinese. Such hypotheses can take the form oftranslated information related to a concept and presented on the cellphone in both English and Chinese, and can be further ranked accordingto merit score indicating a likelihood of a match between the Englishand Chinese conceptual translations. This approach allows both theEnglish language speaker and Chinese speaker to identify their actualmeaning.

Still another type of reasoning engine can include game-based reasoningengines, which can be incorporated into computer games. The reasoningengines can observe one or more players in a game to generate hypothesesabout how a player or players play the game as individuals or in groups.As the reasoning engine develops hypotheses, the reasoning engine'svalidation module can alter the game behavior to seek validation of theplayers' behaviors. If validated, or even invalidated, the reasoningengine can then cause the game to alter its AI behaviors to increase ordecrease the difficulty of the game. Thus, the reasoning engine canconduct automated experimentation on one or more game players toincrease the play value of the game. In view that the reasoning engineis able to monitor more than one data source, the reasoning engine canobserve the players' real-world reactions to the game and determinetheir emotional response to the changes, which can be mapped to a “fun”value for the players.

Yet another example of practical application of reasoning enginesincludes robotic reasoning engines. Reasoning engines can be deployed invarious house hold or commercial robots allowing the robots to reasonand learn how best to operate in their environments. Consider theiRobot® Roomba®, a popular consumer grade robotic vacuum cleaner. Such acleaner can incorporate a reasoning engine that aids in how best tooperate. For example, the reasoning engine can observe how much dirt isdetected during vacuuming and a time of day during operation. Based onsuch environmental data, the cleaner can generate a hypothesis on whenit would be best to vacuum a room for maximal benefit. Although theprevious example is simply, the concepts can be extended to humanoidrobots, which can be “trained” to accomplish human tasks. Examplehumanoid robots that could leverage the inventive subject matter includeNASA's Robonaut, or Honda's Asimo robot. Still further, the hypothesescan be used by robots for navigation a in real-world environment and inall three dimensions. Through exploration and experimentation in anenvironment, the reasoning engine can provide feedback to the robot onhow to adapt its view of the world or how to adapt its navigationalgorithms. One should appreciate that robotic reasoning engines canfunction at different levels within the robot; at the whole robot level(e.g., body, etc.), at the extremity level (e.g., arm, leg, head, etc.),at the digit level (e.g., finger, toe, etc.), or even at the “cognitive”level (i.e., internal processing). These approaches can be used byreasoning engines to discover how to map brain waves to desired motionson a robotic prosthetic of a patient.

Search engines can also benefit from application of the disclosedreasoning engine techniques. Rather than merely providing search resultsback to a user, search engines can be configured to return a reason whya result set is constructed or returned. Therefore, the inventivesubject matter is considered to include adapting existing search engineto provide reasoning services to users.

It should be apparent to those skilled in the art that many moremodifications besides those already described are possible withoutdeparting from the inventive concepts herein. The inventive subjectmatter, therefore, is not to be restricted except in the scope of theappended claims. Moreover, in interpreting both the specification andthe claims, all terms should be interpreted in the broadest possiblemanner consistent with the context. In particular, the terms “comprises”and “comprising” should be interpreted as referring to elements,components, or steps in a non-exclusive manner, indicating that thereferenced elements, components, or steps may be present, or utilized,or combined with other elements, components, or steps that are notexpressly referenced. Where the specification claims refers to at leastone of something selected from the group consisting of A, B, C . . . andN, the text should be interpreted as requiring only one element from thegroup, not A plus N, or B plus N, etc.

1-30. (canceled)
 31. A patient data analysis engine system, comprising:at least one computer readable memory storing software reasoninginstructions; a data interface coupled with at least one sensorassociated with a patient; at least one processor coupled with the datainterface and the at least one computer readable memory, and uponexecution of the software reasoning instructions performs the followingsteps: acquiring environmental data associated with the patient and atleast partially including sensor data from the at least one sensoracquired via the data interface; recognizing aspects from theenvironmental data as target objects; generating at least one hypothesisregarding a correlation among the target objects with respect to anoutcome; and causing an output device to present the at least onehypothesis as relating to the outcome.
 32. The system of claim 31,wherein the patient data analysis engine comprises a virtual machine.33. The system of claim 31, wherein the environmental data represents atleast in part the patient's body.
 34. The system of claim 31, whereinthe environmental data further comprises multiple data modalities. 35.The system of claim 31, wherein the environmental data further comprisesat least one of the following data modalities: biometric data, medicaldata, demographic data, a trend in a body temperature, and identitydata.
 36. The system of claim 31, wherein the environmental data furthercomprises at least one observed change with respect to the patient. 37.The system of claim 36, wherein the at least one observed change withrespect to the patient includes a body change with respect at least oneof the following: a pressure point, airway patency, respiration, bloodpressure, perspiration, a body fluid, and perfusion.
 38. The system ofclaim 31, wherein the environmental data further comprises at least oneof the following observed changes with respect an electricalcharacteristic: a resistance, a current, a voltage, capacitance, areactance, an inductance, a permeability, and a permittivity.
 39. Thesystem of claim 31, wherein the step of generating the hypothesis occursin real-time relative to acquiring the environment data.
 40. The systemof claim 31, wherein the environmental data comprises ambient dataassociated with the patient.
 41. The system of claim 31, wherein theenvironmental data comprises a digital representation of a sceneassociated with the patient.
 42. The system of claim 31, furthercomprising a knowledge base storing object information for known targetobjects, wherein the knowledge base is coupled with the at least oneprocessor.
 43. The system of claim 42, wherein the knowledge base ispersonalized to the patient.
 44. The system of claim 31, wherein thestep of generating the at least one hypothesis further includes formingthe at least one hypothesis according to a least one reasoning rulesset.
 45. The system of claim 44, wherein the step of generating the atleast one hypothesis further includes selecting the rules set based onthe environment data.
 46. The system of claim 45, wherein the rules setincludes at least one of the following types of rules sets: deductiverules, abductive rules, inductive rules, ampliative reasoning rules,case-based reasoning rules, metaphorical mapping rules, forward chainingrules, backward chaining rules, analogical reasoning rules,cause-and-effect reasoning rules, defeasible or indefeasible reasoningrules, comparative reasoning rules, conditional reasoning rules,decompositional reasoning rules, exemplar reasoning, modal logic rules,set-based reasoning rules, systemic reasoning rules, syllogisticreasoning rules, probabilistic reasoning rules, paraconsistent reasoningrules and fuzzy logic rules.
 47. The system of claim 31, wherein theoutcome comprises a negative outcome.
 48. The system of claim 31,wherein the outcome comprises a positive outcome.
 49. The system ofclaim 31, wherein the step of presenting the at least one hypothesiscomprises offering a recommendation on a change to a treatment protocolrelated to the outcome.
 50. A computer-based reasoning system forproviding recommendations based on observed objects in an environment,comprising: a data interface configured to receive sensor datarepresenting a digital representation of a scene of the environmenthaving at least one object; at least one inference engine coupled withinthe data interface and configured to: acquire environmental dataassociated with the patient and at least partially including sensor datafrom the at least one sensor acquired via the data interface; recognizeaspects from the environmental data as target objects; generate at leastone hypothesis regarding a correlation among the target objects withrespect to an outcome; and cause an output device to present the atleast one hypothesis as relating to the outcome.
 51. A computer programproduct embedded in a non-transitory computer readable medium comprisinginstructions executable by a computer processor to perform operationscomprising: acquiring environmental data associated with the patient andat least partially including sensor data from the at least one sensoracquired via the data interface; recognizing aspects from theenvironmental data as target objects; generating at least one hypothesisregarding a correlation among the target objects with respect to anoutcome; and causing an output device to present the at least onehypothesis as relating to the outcome.